In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
In [2]:
import os
In [3]:
os.getcwd()
Out[3]:
'C:\\Users\\delld'
In [4]:
os.chdir('D:\\eda projects')
In [5]:
data=pd.read_csv("Shark Tank India Dataset.csv")
In [6]:
data.head()
Out[6]:
episode_number pitch_number brand_name idea deal pitcher_ask_amount ask_equity ask_valuation deal_amount deal_equity ... ashneer_deal anupam_deal aman_deal namita_deal vineeta_deal peyush_deal ghazal_deal total_sharks_invested amount_per_shark equity_per_shark
0 1 1 BluePine Industries Frozen Momos 1 50.0 5.0 1000.00 75.0 16.00 ... 1 0 1 0 1 0 0 3 25.0 5.333333
1 1 2 Booz scooters Renting e-bike for mobility in private spaces 1 40.0 15.0 266.67 40.0 50.00 ... 1 0 0 0 1 0 0 2 20.0 25.000000
2 1 3 Heart up my Sleeves Detachable Sleeves 1 25.0 10.0 250.00 25.0 30.00 ... 0 1 0 0 1 0 0 2 12.5 15.000000
3 2 4 Tagz Foods Healthy Potato Chips 1 70.0 1.0 7000.00 70.0 2.75 ... 1 0 0 0 0 0 0 1 70.0 2.750000
4 2 5 Head and Heart Brain Development Course 0 50.0 5.0 1000.00 0.0 0.00 ... 0 0 0 0 0 0 0 0 0.0 0.000000

5 rows × 28 columns

In [7]:
data.shape
Out[7]:
(117, 28)
In [ ]:
 
In [8]:
data.describe
Out[8]:
<bound method NDFrame.describe of      episode_number  pitch_number           brand_name  \
0                 1             1  BluePine Industries   
1                 1             2        Booz scooters   
2                 1             3  Heart up my Sleeves   
3                 2             4           Tagz Foods   
4                 2             5       Head and Heart   
..              ...           ...                  ...   
112              34           113        Green Protein   
113              34           114              On2Cook   
114              35           115        Jain Shikanji   
115              35           116                Woloo   
116              35           117         Elcare India   

                                              idea  deal  pitcher_ask_amount  \
0                                     Frozen Momos     1                50.0   
1    Renting e-bike for mobility in private spaces     1                40.0   
2                               Detachable Sleeves     1                25.0   
3                             Healthy Potato Chips     1                70.0   
4                         Brain Development Course     0                50.0   
..                                             ...   ...                 ...   
112                            Plant-Based Protein     0                60.0   
113                         Fastest Cooking Device     0               100.0   
114                                       Lemonade     1                40.0   
115                                Washroom Finder     0                50.0   
116                           Carenting for Elders     0               100.0   

     ask_equity  ask_valuation  deal_amount  deal_equity  ...  ashneer_deal  \
0           5.0        1000.00         75.0        16.00  ...             1   
1          15.0         266.67         40.0        50.00  ...             1   
2          10.0         250.00         25.0        30.00  ...             0   
3           1.0        7000.00         70.0         2.75  ...             1   
4           5.0        1000.00          0.0         0.00  ...             0   
..          ...            ...          ...          ...  ...           ...   
112         2.0        3000.00          0.0         0.00  ...             0   
113         1.0       10000.00          0.0         0.00  ...             0   
114         8.0         500.00         40.0        30.00  ...             1   
115         4.0        1250.00          0.0         0.00  ...             0   
116         2.5        4000.00          0.0         0.00  ...             0   

     anupam_deal  aman_deal  namita_deal  vineeta_deal  peyush_deal  \
0              0          1            0             1            0   
1              0          0            0             1            0   
2              1          0            0             1            0   
3              0          0            0             0            0   
4              0          0            0             0            0   
..           ...        ...          ...           ...          ...   
112            0          0            0             0            0   
113            0          0            0             0            0   
114            1          1            0             1            0   
115            0          0            0             0            0   
116            0          0            0             0            0   

     ghazal_deal  total_sharks_invested  amount_per_shark  equity_per_shark  
0              0                      3              25.0          5.333333  
1              0                      2              20.0         25.000000  
2              0                      2              12.5         15.000000  
3              0                      1              70.0          2.750000  
4              0                      0               0.0          0.000000  
..           ...                    ...               ...               ...  
112            0                      0               0.0          0.000000  
113            0                      0               0.0          0.000000  
114            0                      4              10.0          7.500000  
115            0                      0               0.0          0.000000  
116            0                      0               0.0          0.000000  

[117 rows x 28 columns]>
In [9]:
data.isnull().sum()
Out[9]:
episode_number           0
pitch_number             0
brand_name               0
idea                     0
deal                     0
pitcher_ask_amount       0
ask_equity               0
ask_valuation            0
deal_amount              0
deal_equity              0
deal_valuation           0
ashneer_present          0
anupam_present           0
aman_present             0
namita_present           0
vineeta_present          0
peyush_present           0
ghazal_present           0
ashneer_deal             0
anupam_deal              0
aman_deal                0
namita_deal              0
vineeta_deal             0
peyush_deal              0
ghazal_deal              0
total_sharks_invested    0
amount_per_shark         0
equity_per_shark         0
dtype: int64
In [10]:
#total deal occur in this session
data['deal'].value_counts()
Out[10]:
1    65
0    52
Name: deal, dtype: int64
In [11]:
#Five shark invested deals
All_sharks=data[data["total_sharks_invested"]==5]
All_sharks
Out[11]:
episode_number pitch_number brand_name idea deal pitcher_ask_amount ask_equity ask_valuation deal_amount deal_equity ... ashneer_deal anupam_deal aman_deal namita_deal vineeta_deal peyush_deal ghazal_deal total_sharks_invested amount_per_shark equity_per_shark
15 6 16 Skippi Pops Ice-Pops 1 45.0 5.0 900.0 100.0 15.0 ... 1 1 1 1 1 0 0 5 20.0 3.0
49 17 50 Find Your Kicks India Sneaker Resale 1 50.0 10.0 500.0 50.0 25.0 ... 1 1 1 1 0 1 0 5 10.0 5.0
63 20 64 IN A CAN Can Cocktails 1 50.0 2.0 2500.0 100.0 10.0 ... 1 1 1 1 0 1 0 5 20.0 2.0
79 25 80 Sunfox Technologies Portable ECG Device 1 100.0 2.0 5000.0 100.0 6.0 ... 0 1 0 1 1 1 1 5 20.0 1.2

4 rows × 28 columns

In [12]:
import plotly.express as px
In [13]:
All_sharks=data[data["total_sharks_invested"]==5]
figure=px.bar(All_sharks,x='brand_name',y='deal_amount',title='total investment & deal brands',text_auto=True,color='pitcher_ask_amount',template="plotly_dark")
figure.show()
In [14]:
#highest pitch ask amount
high=data[data['pitcher_ask_amount']>=100]
high.shape
Out[14]:
(23, 28)
In [15]:
figure1=px.bar(high,x='brand_name',y='pitcher_ask_amount',color='deal_amount',title='high_pitcher_ask_amount',height=400,template='plotly_dark',text_auto=True)
figure1.show()
In [16]:
#Ask equity and deal euity of highest pitcher_aks_amount_Brand
figure2=px.bar(high,x='brand_name',y='ask_equity',color='deal_equity',title='Ask equity & Deal_equity of highest picher brand',height=500)
figure2.show()
In [17]:
#Least pitch ask amount
low=data[data['pitcher_ask_amount']<=25][0:10]
low.shape
Out[17]:
(7, 28)
In [18]:
figure4=px.bar(low,x='brand_name',y='pitcher_ask_amount',color='deal_amount',template='plotly_dark',title='Least Pitcher ask amt')
figure4.show()
In [19]:
#total no.of deals
sharks=['Ashneer','Anupam','Namita','Vineeta','Aman','Peyush','Ghazal']
total_deal_sharks=[data.ashneer_deal.sum(),data.anupam_deal.sum(),data.namita_deal.sum(),data.vineeta_deal.sum(),data.aman_deal.sum(),data.peyush_deal.sum(),data.ghazal_deal.sum()]

figure5=px.bar(All_sharks,x=sharks,y=total_deal_sharks,title='Deal done by each shark',color=sharks,template='plotly_dark')
figure5.show()
In [ ]: